NDLI: Nonlinear face recognition based on maximum average margin criterion

Content Provider	IEEE Xplore Digital Library
Author	Baochang Zhang Xilin Chen Shiguang Shan Wen Gao
Copyright Year	2005
Description	Author affiliation: Comput. Sch., Harbin Inst. of Technol., China (Baochang Zhang; Xilin Chen)
Abstract	This paper proposes a novel nonlinear discriminant analysis method named by kernerlized maximum average margin criterion (KMAMC), which has combined the idea of support vector machine with the kernel fisher discriminant analysis (KFD). We also use a simple method to prove the relationship between both kernel methods. The difference of KMAMC from traditional KFD methods include: (1) the within-class and between-class scatter matrices are computed based on the support vectors instead of all the samples; (2) multiple centers are exploited instead of the single center in computing the two scatter matrices; (3) the discriminant criteria is formulated as subtracting the trace of within-class scatter matrix from that of the between-class scatter matrix, therefore, the tedious singularity problem is avoided. These features have made KMAMC more practical for real-world applications. Our experiments on two face databases, the FERET and CAS-PEAL face database, have illustrated its excellent performance compared with some traditional methods such as Eigenface, Fisherface, and KFD.
Sponsorship	IEEE Comput. Soc
Starting Page	554
Ending Page	559
File Size	208698
Page Count	6
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.247
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Scattering Kernel Principal component analysis Support vector machines Spatial databases Feature extraction Research and development Content addressable storage Linear discriminant analysis Support Vector Machine Face Recognition Kernel Fisher
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An Efficient Method of Nonlinear Feature Extraction Based on SVM

A improved method of face recognition by kernel maximum margin criterion

A New Two-Phase Method of Face Recognition Based on Image Matrix

Nonlinear face recognition based on maximum average margin criterion (2005)

Bilinear Analysis for Kernel Selection and Nonlinear Feature Extraction

Modified kernel-based nonlinear feature extraction [face recognition example]

Facial recognition/verification using Gabor wavelets and kernel methods

Kernel Fisher Discriminant Analysis for Palmprint Recognition

Face classification based on Shannon wavelet kernel and modified Fisher criterion

Nonlinear face recognition based on maximum average margin criterion

Similar Documents

An Efficient Method of Nonlinear Feature Extraction Based on SVM

A improved method of face recognition by kernel maximum margin criterion

A New Two-Phase Method of Face Recognition Based on Image Matrix

Nonlinear face recognition based on maximum average margin criterion (2005)

Bilinear Analysis for Kernel Selection and Nonlinear Feature Extraction

Modified kernel-based nonlinear feature extraction [face recognition example]

Facial recognition/verification using Gabor wavelets and kernel methods

Kernel Fisher Discriminant Analysis for Palmprint Recognition

Face classification based on Shannon wavelet kernel and modified Fisher criterion

Nonlinear face recognition based on maximum average margin criterion